Pushing the limits of non-autoregressive speech recognition
We combine recent advancements in end-to-end speech recognition to non-autoregressive
automatic speech recognition. We push the limits of non-autoregressive state-of-the-art
results for multiple datasets: LibriSpeech, Fisher+ Switchboard and Wall Street Journal. Key
to our recipe, we leverage CTC on giant Conformer neural network architectures with
SpecAugment and wav2vec2 pre-training. We achieve 1.8%/3.6% WER on LibriSpeech
test/test-other sets, 5.1%/9.8% WER on Switchboard, and 3.4% on the Wall Street Journal …
automatic speech recognition. We push the limits of non-autoregressive state-of-the-art
results for multiple datasets: LibriSpeech, Fisher+ Switchboard and Wall Street Journal. Key
to our recipe, we leverage CTC on giant Conformer neural network architectures with
SpecAugment and wav2vec2 pre-training. We achieve 1.8%/3.6% WER on LibriSpeech
test/test-other sets, 5.1%/9.8% WER on Switchboard, and 3.4% on the Wall Street Journal …
We combine recent advancements in end-to-end speech recognition to non-autoregressive automatic speech recognition. We push the limits of non-autoregressive state-of-the-art results for multiple datasets: LibriSpeech, Fisher+Switchboard and Wall Street Journal. Key to our recipe, we leverage CTC on giant Conformer neural network architectures with SpecAugment and wav2vec2 pre-training. We achieve 1.8%/3.6% WER on LibriSpeech test/test-other sets, 5.1%/9.8% WER on Switchboard, and 3.4% on the Wall Street Journal, all without a language model.
arxiv.org
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